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this will all be done within Excel. We will introduce some often used and important

inferential statistics techniques in this chapter.

6.2 Let the Statistical Technique Fit the Data

Consider the type of sample data we have seen thus far in Chaps. 1–5. In just about

every case, the data has contained a combination of quantitative and qualitative data

elements. For example, the data for teens visiting websites in Chap. 3 provided the

number of page views for each teen, and also described the circumstances related

to the page views—either
new
or
old
site. This was our ﬁrst exposure to sophisti-

cated statistics and to
cause and effect
analysis—one variable causing an effect on

another. We can think of these categories, new and old, as experimental
treatments
,

and the page views as a
response variable
. Thus, the treatment is the assumed

cause and the effect is the number of views. In an attempt to determine if the sam-

ple means of the two treatments were different or equal, we performed an analysis

called a
paired t-Test
. This test permitted us to consider complicated questions.

So when do we need this
more
sophisticated statistical analysis? Some of the

answers to this question can be summarized as follows:

1. When we want to make a precise mathematical statement about the data’s

capability to infer characteristics of the population.

2. When we want to determine how closely these data ﬁt some assumed model of

behavior.

3. When we need a higher level of analysis to further investigate the preliminary

ﬁndings of descriptive and exploratory analysis.

This chapter will focus on data that has both qualitative and quantitative com-

ponents, but we will also consider data that is strictly qualitative (categorical), as

you will soon see. By no means can we explore the exhaustive set of statistical

techniques available for these data types; there are thousands of techniques avail-

able and more are being developed as we speak. But, we will introduce some of the

most often used tools in statistical analysis. Finally, I repeat that it is important to

remember that the type of data we are analyzing will dictate the technique that we

can employ. The misapplication of a technique on a particular set of data is the most

common reason for dismissing or ignoring the results of an analysis; the analysis

just does not match the data.

2
—Chi-Square Test of Independence for Categorical Data

6.3

χ

Let us begin with a powerful analytical tool applied to a frequently occurring type

of data—categorical variables. In this analysis, a test is conducted on sample data,

and the test attempts to determine if there is an association, or relationship, between

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